from builtins import range
from builtins import object
import numpy as np

from cs231n.layers import *
from cs231n.layer_utils import *


class TwoLayerNet(object):
    """
    A two-layer fully-connected neural network with ReLU nonlinearity and
    softmax loss that uses a modular layer design. We assume an input dimension
    of D, a hidden dimension of H, and perform classification over C classes.

    The architecure should be affine - relu - affine - softmax.

    Note that this class does not implement gradient descent; instead, it
    will interact with a separate Solver object that is responsible for running
    optimization.

    The learnable parameters of the model are stored in the dictionary
    self.params that maps parameter names to numpy arrays.
    """

    def __init__(self, input_dim=3*32*32, hidden_dim=100, num_classes=10,
                 weight_scale=1e-3, reg=0.0):
        """
        Initialize a new network.

        Inputs:
        - input_dim: An integer giving the size of the input
        - hidden_dim: An integer giving the size of the hidden layer
        - num_classes: An integer giving the number of classes to classify
        - dropout: Scalar between 0 and 1 giving dropout strength.
        - weight_scale: Scalar giving the standard deviation for random
          initialization of the weights.
        - reg: Scalar giving L2 regularization strength.
        """
        self.params = {}
        self.reg = reg

        ############################################################################
        # TODO: Initialize the weights and biases of the two-layer net. Weights    #
        # should be initialized from a Gaussian with standard deviation equal to   #
        # weight_scale, and biases should be initialized to zero. All weights and  #
        # biases should be stored in the dictionary self.params, with first layer  #
        # weights and biases using the keys 'W1' and 'b1' and second layer weights #
        # and biases using the keys 'W2' and 'b2'.                                 #
        ############################################################################
        W1 = weight_scale * np.random.randn(input_dim, hidden_dim)
        B1 = np.zeros((1, hidden_dim))
        W2 = weight_scale * np.random.randn(hidden_dim, num_classes)
        B2 = np.zeros((1, num_classes))
        self.params = {'W1':W1, 'b1':B1, 'W2':W2, 'b2':B2}
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################


    def loss(self, X, y=None):
        """
        Compute loss and gradient for a minibatch of data.

        Inputs:
        - X: Array of input data of shape (N, d_1, ..., d_k)
        - y: Array of labels, of shape (N,). y[i] gives the label for X[i].

        Returns:
        If y is None, then run a test-time forward pass of the model and return:
        - scores: Array of shape (N, C) giving classification scores, where
          scores[i, c] is the classification score for X[i] and class c.

        If y is not None, then run a training-time forward and backward pass and
        return a tuple of:
        - loss: Scalar value giving the loss
        - grads: Dictionary with the same keys as self.params, mapping parameter
          names to gradients of the loss with respect to those parameters.
        """
        scores = None
        ############################################################################
        # TODO: Implement the forward pass for the two-layer net, computing the    #
        # class scores for X and storing them in the scores variable.              #
        ############################################################################
        out1, cache1 = affine_relu_forward(X, self.params['W1'], self.params['b1'])
        scores, cache2 = affine_forward(out1, self.params['W2'], self.params['b2'])
        #print(('scores is: ', scores))
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # If y is None then we are in test mode so just return scores
        if y is None:
            return scores

        loss, grads = 0, {}
        ############################################################################
        # TODO: Implement the backward pass for the two-layer net. Store the loss  #
        # in the loss variable and gradients in the grads dictionary. Compute data #
        # loss using softmax, and make sure that grads[k] holds the gradients for  #
        # self.params[k]. Don't forget to add L2 regularization!                   #
        #                                                                          #
        # NOTE: To ensure that your implementation matches ours and you pass the   #
        # automated tests, make sure that your L2 regularization includes a factor #
        # of 0.5 to simplify the expression for the gradient.                      #
        ############################################################################
        dims = X.shape[1:]
        xr = np.reshape(X, (X.shape[0], np.prod(dims)))
        
        dataloss, dataDx = softmax_loss(scores, y)
        reg_loss = 0.5 * self.reg * np.sum(self.params['W1'] * self.params['W1']) + 0.5 * self.reg * np.sum(self.params['W2'] * self.params['W2'])
        loss= dataloss + reg_loss
        #print(('loss is: ', loss))
        
        # backpropate the gradient to the parameters
        # first backprop into parameters W2 and b2
        dW2 = np.dot(out1.T, dataDx)
        db2 = np.sum(dataDx, axis=0, keepdims=True)
        
        # next backprop into hidden layer
        dhidden = np.dot(dataDx, self.params['W2'].T)
        # backprop the ReLU non-linearity
        dhidden[out1 <= 0] = 0
        # finally into W1,b1
        dW1 = np.dot(xr.T, dhidden)
        db1 = np.sum(dhidden, axis=0, keepdims=True)
  
        # add regularization gradient contribution
        dW2 += self.reg * self.params['W2']
        dW1 += self.reg * self.params['W1']
        
        grads = {'W1':dW1, 'b1':db1, 'W2':dW2, 'b2':db2}
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        return loss, grads


class FullyConnectedNet(object):
    """
    A fully-connected neural network with an arbitrary number of hidden layers,
    ReLU nonlinearities, and a softmax loss function. This will also implement
    dropout and batch normalization as options. For a network with L layers,
    the architecture will be

    {affine - [batch norm] - relu - [dropout]} x (L - 1) - affine - softmax

    where batch normalization and dropout are optional, and the {...} block is
    repeated L - 1 times.

    Similar to the TwoLayerNet above, learnable parameters are stored in the
    self.params dictionary and will be learned using the Solver class.
    """

    def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10,
                 dropout=0, use_batchnorm=False, reg=0.0,
                 weight_scale=1e-2, dtype=np.float32, seed=None):
        """
        Initialize a new FullyConnectedNet.

        Inputs:
        - hidden_dims: A list of integers giving the size of each hidden layer.
        - input_dim: An integer giving the size of the input.
        - num_classes: An integer giving the number of classes to classify.
        - dropout: Scalar between 0 and 1 giving dropout strength. If dropout=0 then
          the network should not use dropout at all.
        - use_batchnorm: Whether or not the network should use batch normalization.
        - reg: Scalar giving L2 regularization strength.
        - weight_scale: Scalar giving the standard deviation for random
          initialization of the weights.
        - dtype: A numpy datatype object; all computations will be performed using
          this datatype. float32 is faster but less accurate, so you should use
          float64 for numeric gradient checking.
        - seed: If not None, then pass this random seed to the dropout layers. This
          will make the dropout layers deteriminstic so we can gradient check the
          model.
        """
        self.use_batchnorm = use_batchnorm
        self.use_dropout = dropout > 0
        self.reg = reg
        self.num_layers = 1 + len(hidden_dims)
        self.dtype = dtype
        self.params = {}

        ############################################################################
        # TODO: Initialize the parameters of the network, storing all values in    #
        # the self.params dictionary. Store weights and biases for the first layer #
        # in W1 and b1; for the second layer use W2 and b2, etc. Weights should be #
        # initialized from a normal distribution with standard deviation equal to  #
        # weight_scale and biases should be initialized to zero.                   #
        #                                                                          #
        # When using batch normalization, store scale and shift parameters for the #
        # first layer in gamma1 and beta1; for the second layer use gamma2 and     #
        # beta2, etc. Scale parameters should be initialized to one and shift      #
        # parameters should be initialized to zero.                                #
        ############################################################################
        for idx, value in enumerate(hidden_dims):
            if idx == 0:
                prevDim = input_dim
            else:
                prevDim = hidden_dims[idx - 1]
                
            self.params['W%s' % (idx + 1)] = weight_scale * np.random.randn(prevDim, hidden_dims[idx])
            self.params['b%s' % (idx + 1)] = np.zeros((1, hidden_dims[idx]))
        
        self.params['W%s' % (len(hidden_dims) + 1)] = weight_scale * np.random.randn(hidden_dims[len(hidden_dims) - 1], num_classes)
        self.params['b%s' % (len(hidden_dims) + 1)] = np.zeros((1, num_classes))
            
        #print(('self.params: ', self.params))
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # When using dropout we need to pass a dropout_param dictionary to each
        # dropout layer so that the layer knows the dropout probability and the mode
        # (train / test). You can pass the same dropout_param to each dropout layer.
        self.dropout_param = {}
        if self.use_dropout:
            self.dropout_param = {'mode': 'train', 'p': dropout}
            if seed is not None:
                self.dropout_param['seed'] = seed

        # With batch normalization we need to keep track of running means and
        # variances, so we need to pass a special bn_param object to each batch
        # normalization layer. You should pass self.bn_params[0] to the forward pass
        # of the first batch normalization layer, self.bn_params[1] to the forward
        # pass of the second batch normalization layer, etc.
        self.bn_params = []
        if self.use_batchnorm:
            self.bn_params = {'bn_param' + str(i + 1): {'mode': 'train',
                                                        'running_mean': np.zeros(hidden_dims[i]),
                                                        'running_var': np.zeros(hidden_dims[i])}
                              for i in range(len(hidden_dims))}
            gammas = {'gamma' + str(i + 1): np.ones(hidden_dims[i]) for i in range(len(hidden_dims))}
            betas = {'beta' + str(i + 1): np.zeros(hidden_dims[i]) for i in range(len(hidden_dims))}

            self.params.update(betas)
            self.params.update(gammas)

        # Cast all parameters to the correct datatype
        for k, v in self.params.items():
            self.params[k] = v.astype(dtype)


    def loss(self, X, y=None):
        """
        Compute loss and gradient for the fully-connected net.

        Input / output: Same as TwoLayerNet above.
        """
        X = X.astype(self.dtype)
        mode = 'test' if y is None else 'train'

        # Set train/test mode for batchnorm params and dropout param since they
        # behave differently during training and testing.
        if self.use_dropout:
            self.dropout_param['mode'] = mode
        if self.use_batchnorm:
            for key, bn_param in self.bn_params.items():
                bn_param[mode] = mode

        scores = None
        ############################################################################
        # TODO: Implement the forward pass for the fully-connected net, computing  #
        # the class scores for X and storing them in the scores variable.          #
        #                                                                          #
        # When using dropout, you'll need to pass self.dropout_param to each       #
        # dropout forward pass.                                                    #
        #                                                                          #
        # When using batch normalization, you'll need to pass self.bn_params[0] to #
        # the forward pass for the first batch normalization layer, pass           #
        # self.bn_params[1] to the forward pass for the second batch normalization #
        # layer, etc.                                                              #
        ############################################################################
        out = {}
        caches = {}
        for idx in range(1, self.num_layers):
            if idx == 1:
                data = X
            else:
                data = out[idx - 1]
                
            if self.use_batchnorm:
                gamma = self.params['gamma' + str(idx)]
                beta = self.params['beta' + str(idx)]
                bn_param = self.bn_params['bn_param' + str(idx)]
            
            if self.use_batchnorm:
                out[idx], caches[idx] = affine_norm_relu_forward(data, self.params['W%s' % (idx)], self.params['b%s' % (idx)], gamma, beta, bn_param)
            else:
                out[idx], caches[idx] = affine_relu_forward(data, self.params['W%s' % (idx)], self.params['b%s' % (idx)])
            
        out[self.num_layers], caches[self.num_layers]= affine_forward(out[self.num_layers - 1], self.params['W%s' % (self.num_layers)], self.params['b%s' % (self.num_layers)])
        
        scores = out[self.num_layers]
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        # If test mode return early
        if mode == 'test':
            return scores

        loss, grads = 0.0, {}
        ############################################################################
        # TODO: Implement the backward pass for the fully-connected net. Store the #
        # loss in the loss variable and gradients in the grads dictionary. Compute #
        # data loss using softmax, and make sure that grads[k] holds the gradients #
        # for self.params[k]. Don't forget to add L2 regularization!               #
        #                                                                          #
        # When using batch normalization, you don't need to regularize the scale   #
        # and shift parameters.                                                    #
        #                                                                          #
        # NOTE: To ensure that your implementation matches ours and you pass the   #
        # automated tests, make sure that your L2 regularization includes a factor #
        # of 0.5 to simplify the expression for the gradient.                      #
        ############################################################################
        dataloss, dataDx = softmax_loss(scores, y)
        
        dims = X.shape[1:]
        xr = np.reshape(X, (X.shape[0], np.prod(dims)))
        
        #calcuate the regularition error
        reg_loss = 0
        for idx in range(1, self.num_layers + 1):
            reg_loss +=  0.5 * self.reg * np.sum(self.params['W%s' % (idx)] * self.params['W%s' % (idx)])
        
        loss= dataloss + reg_loss

        
        # backpropate the gradient to the parameters
        dgamma = {}
        dbeta = {}
        for idx in reversed(range(1, self.num_layers + 1)):
            #if idx == self.num_layers:
             #   dout = dataDx
            #else:
             #   dout = np.dot(dout, self.params['W%s' % (idx + 1)].T)
                #excpet the last layer, all the layer need to calcuate RELU back gradident
              #  dout[out[idx] <= 0] = 0
            
            if idx == 1:
                xin = xr
            else:
                xin = out[idx - 1]
                
            if idx == self.num_layers:
                dout = dataDx
                dout, grads['W%s' % (idx)], grads['b%s' % (idx)] = affine_backward(dout, caches[idx])
            else:
                if self.use_batchnorm:
                    dout, grads['W%s' % (idx)], grads['b%s' % (idx)], grads['gamma%s' % (idx)], grads['beta%s' % (idx)] = affine_norm_relu_backward(dout, caches[idx])
                else:
                    dout, grads['W%s' % (idx)], grads['b%s' % (idx)] = affine_relu_backward(dout, caches[idx])


            #grads['W%s' % (idx)] = np.dot(xin.T, dout)
            # add regularization gradient contribution
            grads['W%s' % (idx)] += self.reg * self.params['W%s' % (idx)]
            #grads['b%s' % (idx)] = np.sum(dout, axis=0, keepdims=True)
        
        #dW2 = np.dot(out1.T, dataDx)
        #db2 = np.sum(dataDx, axis=0, keepdims=True)
        
        # next backprop into hidden layer
        #dhidden = np.dot(dataDx, self.params['W2'].T)
        # backprop the ReLU non-linearity
        #dhidden[out1 <= 0] = 0
        # finally into W1,b1
        #dW1 = np.dot(xr.T, dhidden)
        #db1 = np.sum(dhidden, axis=0, keepdims=True)
  
        # add regularization gradient contribution
        #dW2 += self.reg * self.params['W2']
        #dW1 += self.reg * self.params['W1']
        
        #grads = {'W1':dW1, 'b1':db1, 'W2':dW2, 'b2':db2}
        ############################################################################
        #                             END OF YOUR CODE                             #
        ############################################################################

        return loss, grads
    
def affine_norm_relu_forward(x, w, b, gamma, beta, bn_param):
    """
            Convenience layer that perorms an affine transform followed by a ReLU
            Inputs:
            - x: Input to the affine layer
            - w, b: Weights for the affine layer
            - gamma, beta : Weight for the batch norm regularization
            - bn_params : Contain variable use to batch norml, running_mean and var
            Returns a tuple of:
            - out: Output from the ReLU
            - cache: Object to give to the backward pass
            """

    h, h_cache = affine_forward(x, w, b)
    hnorm, hnorm_cache = batchnorm_forward(h, gamma, beta, bn_param)
    hnormrelu, relu_cache = relu_forward(hnorm)
    cache = (h_cache, hnorm_cache, relu_cache)

    return hnormrelu, cache


def affine_norm_relu_backward(dout, cache):
    """
        Backward pass for the affine-relu convenience layer
        """
    h_cache, hnorm_cache, relu_cache = cache

    dhnormrelu = relu_backward(dout, relu_cache)
    dhnorm, dgamma, dbeta = batchnorm_backward_alt(dhnormrelu, hnorm_cache)
    dx, dw, db = affine_backward(dhnorm, h_cache)

    return dx, dw, db, dgamma, dbeta
